Hepatocellular carcinoma (HCC) is a common lethal malignancy and among the five leading causes of cancer death worldwide. The incidence is rising in the United States, UK and Japan. Liver cancer is the second major cause of cancer death in China. Epidemiological studies have shown that hepatitis B and C virus infections, alcohol-induced liver injury and consumption of aflatoxin are closely associated with liver cancer. Extensive studies have been performed to better understand the clinico-pathological features to improve the clinical management for HCC patients. However, conventional clinico-pathological parameters have limited predictive power, and patients with the same stage of disease can have very different disease outcomes. Microarray technology provides a biological mean to gather large amount of gene expression data on an unbiased basis. Molecular portraits reviewed by the tumors' gene expression patterns have been used to identify new molecular criteria for prognostication of diverse cancer types including breast cancer, prostate cancer, lung cancer and brain tumors.
Using the cDNA microarray approach, the expression profiles of liver cancer cell lines and human samples have been reported. Expression of alpha-fetoprotein (AFP) highlighted the molecular subtypes of HCC cell lines. Deregulation of the cell cycle regulators and genes associated with metabolism have been observed, and the expression profile was associated with the tumor differentiation status. A recent study on prediction of HCC early recurrence by gene expression only reported the intrahepatic recurrence within 1 year in a small patient set and used genechips of 6000 genes. In the present study, the Cox regression and Kaplan-Meier analyses were used on 48 HCCs to identify a set of 26 genes from microarrays printed with 23000 clones. The prognostic gene set was then further delineated to include the top ranked 12 genes, which had an accuracy of 97.8% and 89.3% in predicting disease recurrence and death, respectively, within 3 years after hepatectomy. The gene expression profile thus generated can provide a more accurate prognosis to predict disease recurrence and death compared to the standard systems based on clinical and histological criteria. The result also offers an approach to select patients with poor prognosis for aggressive adjuvant therapy.